Nanosight Analysis

Overall Objective

Load Libraries

library(tidyverse)
library(cowplot)
library(broom)
library(pwr)
library(plotly)

Import data

#setwd("~/GitHub/time-course/data")
setwd("~/Library/Mobile\ Documents/com~apple~CloudDocs/time-course/data")
rawdata <- "revised_MASTER-ExperimentSummary.csv"
timecourse <- "timecourse2017.csv"
data <- read_csv(rawdata)
tc <- read_csv(timecourse, na = c("","NA"))

Convert data from ‘wide’ to ‘long’ format

data1 <- data %>%
  gather(Sample,Count,2:250)
# Separate samples by identifiers 
data2 <- data1 %>% 
  separate(Sample, into=c("Sample_ID","Dilution_factor","Injection","Tech_rep", sep = "_")) %>% 
  select(-`_`)

Factor the data into categorical variables

# Refactoring Columns for samples
data2$Sample_ID <- as.factor(data2$Sample_ID)
data2$Dilution_factor <- as.numeric(data2$Dilution_factor)
data2$Injection<- as.factor(data2$Injection)
data2$Tech_rep <- as.numeric(data2$Tech_rep)
# Refactoring COlumns for timecourse
tc$Sample_ID <- as.factor(tc$Sample_ID)
tc$Day <- as.factor(tc$Day)
tc$Weight <- as.numeric(tc$Weight)
tc$TEI_Day <- as.factor(tc$TEI_Day)

tc1 <- tc %>% 
  select(Day:Pups)
tc1

Back calculate the original concentration of the sample

data2 <- data2 %>% 
  mutate(True_Count=Dilution_factor*Count)
data2

Average the three technical replicate readings

data3 <- data2 %>% 
  group_by(particle_size,Sample_ID,Dilution_factor,Injection) %>% 
  summarise( tech_N = length(True_Count),
             tech_mean = mean(True_Count),
             tech_sd = sd(True_Count),
             tech_se = tech_sd/sqrt(tech_N))
data3

test1 <- left_join(tc1,data3, by= "Sample_ID")

Summarize samples by injection (average both injections)

data4 <- data3 %>% 
  group_by(particle_size,Sample_ID,Dilution_factor) %>% 
  summarise( inj_N = length(tech_mean),
             inj_mean = mean(tech_mean),
             inj_sd = sd(tech_mean),
             inj_se = inj_sd/sqrt(inj_N))
data4

test2 <- left_join(tc1,data4, by= "Sample_ID")
test2

Quick visualizations

Graphing all samples

test1$Sample_ID_correct = factor(test1$Sample_ID, levels=c('1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20','21','22','23','24','25','26','27','28','29','30','31','32','33','34','35','36','70','73','74','75'))
graph1 <- test1 %>%
  ggplot(aes(x=particle_size, y=tech_mean,color=Injection ))+ #plot
  geom_ribbon(aes(ymin=tech_mean-tech_se, ymax=tech_mean+tech_se),alpha=0.2,fill = alpha('grey12', 0.2)) + #error bars
  geom_line(size=2.0) + xlim(0,500)+ #line size, x-axis scale
  scale_y_continuous(expand=c(0,0))+ #set bottom of graph
  xlab("Particle Size") + # X axis label
  ylab("\nMean Particle Concentration/ml\n") + # Y axis label
  ggtitle("Nanosight Histogram of\nVirgin Mouse Plasma")+ #title
  labs(color="Injection")+ #Label table title
  facet_wrap( ~ Sample_ID_correct, nrow=7)
graph1

Graphing averaged samples by experimental day

graph2 <- test2 %>%
  group_by(TEI_Day) %>% 
  ggplot(aes(x=particle_size, y=inj_mean,color=Day ))+ #plot
  #geom_ribbon(aes(ymin=inj_mean-inj_se, ymax=inj_mean+inj_se),alpha=0.2,fill = alpha('grey12', 0.2)) + #error bars
  geom_line(size=2) + xlim(0,500)+ #line size, x-axis scale
  scale_y_continuous(expand=c(0,0))+ #set bottom of graph
  xlab("Particle Size") + # X axis label
  ylab("\nMean Particle Concentration/ml\n") + # Y axis label
  ggtitle("Nanosight Histogram of\nVirgin Mouse Plasma")+ #title
  labs(color="Condition")+ #Label table title
  facet_wrap(~ TEI_Day, ncol=7)
graph2

Particle concentration values for each of the 36 samples

test3 <- test2 %>% 
  group_by(Day,Sample_ID) %>% 
  summarise(particle_conc=sum(inj_mean))
test3

Summary statistics of particle concentration (averaging n=6 for each time point)

test4 <- test3 %>% 
  group_by(Day) %>% 
  summarise(Day_N=length(particle_conc),
            Day_mean = mean(particle_conc),
            Day_sd = sd(particle_conc),
            Day_se = Day_sd/sqrt(Day_N))
test4

Boxplot

plot1 <- test3 %>% 
  filter(!Sample_ID %in% c('6','28','32')) %>% 
  group_by(Day) %>% 
  ggplot(aes(x= Day, y = particle_conc, color=Day)) +
  geom_boxplot(colour="black",fill=NA) + 
  geom_point(aes(text = paste("Sample ID:", Sample_ID)),
             position='jitter',size=3)+
  xlab("\nDay of Gestation\n") + # X axis label
  ylab("\nExosomes/ml\n") + # Y axis label
  ggtitle("Plasma Exosome Concentration\nThroughout Pregnancy\n")+ #title
  labs(color="Condition")+ # Label table title
  scale_x_discrete(breaks=c("1","5","10","14","17","20"),  # Change X axis label
                   labels=c("Virgin","5","10","14","17","1 Day Post")) +
  scale_color_discrete(labels=c("Virgin","5","10","14","17","1 Day Post")) # Change Legend
  
plot1

#ggsave("Exosome_plot.png", height = 5, width = 7, units = "in", dpi = 600)

Plotly

  ggplotly(plot1)

Bar plot

Looking at Variation between the days the samples were run

test7 <- test3 %>% 
  left_join(tc1)

Joining, by = c("Day", "Sample_ID")

plot2 <- test7 %>%
  ggplot(aes(x = Day, y = particle_conc, color = Day, shape=TEI_Day))+
  geom_point(position= 'dodge',size=4)+
  scale_shape_manual(values=c(15,16,17,18,22,23,24))+
  xlab("\nDay of Gestation\n") + # X axis label
  ylab("\nExosomes/ml\n") + # Y axis label
  ggtitle("Plasma Exosome Concentration\nThroughout Pregnancy\n")+ #title
  labs(color="Condition") + # Label table title
  scale_x_discrete(breaks=c("1","5","10","14","17","20"),  # Change X axis label
                   labels=c("Virgin","5","10","14","17","1 Day Post")) +
  scale_color_discrete(labels=c("Virgin","5","10","14","17","1 Day Post"))
plot2

ggplotly(plot2)

Width not defined. Set with `position_dodge(width = ?)`

Looking at nanoparticle range

nano_100 <- data4 %>% 
  filter(particle_size<140.5)
nano_100_data <- left_join(tc1,nano_100, by= "Sample_ID")
nano_100_data_plot <- nano_100_data %>%
  group_by(Day,Sample_ID) %>% 
  summarise(particle_conc=sum(inj_mean)) %>% 
  filter(!Sample_ID %in% c('6','28','32')) %>% 
  ggplot(aes(factor(Day),particle_conc, color=Day)) +
  geom_boxplot(colour="black",fill=NA) + 
  geom_point(position='jitter',size=3)+
  xlab("\nDay of Gestation\n") + # X axis label
  ylab("\nExosomes/ml\n") + # Y axis label
  ggtitle("Plasma Exosome Concentration\nThroughout Pregnancy\n")+ #title
  labs(color="Condition") + # Label table title
  scale_x_discrete(breaks=c("1","5","10","14","17","20"),  # Change X axis label
                   labels=c("Virgin","5","10","14","17","1 Day Post")) +
  scale_color_discrete(labels=c("Virgin","5","10","14","17","1 Day Post"))
nano_100_data_plot   

NA

Statistics

Shapiro test

tidy(shapiro.test(test3$particle_conc))

p value >0.05 therefore conlude data is normally distributed

ANOVA

jacob <- nano_100_data %>%
   group_by(Day,Sample_ID) %>% 
  summarise(particle_conc=sum(inj_mean)) %>% 
  filter(!Sample_ID %in% c('6','28','32'))
         
fit <- aov(particle_conc ~ Day, data=jacob)
stats <- tidy(fit)
stats

Statistically significant, thus Tukey’s HSD post hoc analysis can determine significant differences.

Tukey Post Hoc Test

HSD <- TukeyHSD(fit)
tukey <- tidy(HSD)
tukey

Significant Tukey Post Hoc Test Values

tukey %>%
  filter(adj.p.value<0.05) %>% 
  arrange(adj.p.value)

test8 <- test7 %>% 
  filter(!Day == "20")
fit <- lm(particle_conc ~ Weight ,data = test8)
summary(fit)

Call:
lm(formula = particle_conc ~ Weight, data = test8)

Residuals:
       Min         1Q     Median         3Q        Max 
-2.600e+11 -7.122e+10 -2.456e+10  8.690e+10  2.239e+11 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)   
(Intercept) 5.768e+10  7.666e+10   0.752  0.45727   
Weight      8.548e+09  2.849e+09   3.000  0.00519 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.127e+11 on 32 degrees of freedom
Multiple R-squared:  0.2196,    Adjusted R-squared:  0.1952 
F-statistic: 9.002 on 1 and 32 DF,  p-value: 0.00519

tidy(summary(fit))

test7 %>% 
  ggplot(aes(x= Weight, y = particle_conc))+
  geom_point()+
  geom_smooth()+
  xlab("\nWeight (g)\n") + # X axis label
  ylab("Exosomes/ml\n") + # Y axis label
  ggtitle("Linear Regression of Exosome \nConcentration vs. Weight")+ #title
  labs(color="Day")#Label table title

NA

test7 %>% 
  filter(!Sample_ID == '70') %>% 
  ggplot(aes(x= Weight, y = particle_conc))+
  geom_point(size = 3,aes(color=factor(Day)))+
  geom_smooth(method = "lm", level = 0.95)+
  xlab("\nWeight (g)\n") + # X axis label
  ylab("Exosomes/ml\n") + # Y axis label
  ggtitle("Linear Regression of Exosome \nConcentration vs. Weight")+ #title
  labs(color="Day")#Label table title

test7 %>% 
  ggplot(aes(x= Pups, y = particle_conc))+
  geom_point(size = 4, aes(color=factor(Day)))+
  #geom_smooth(method = "lm", level = 0.95)+
  scale_x_continuous(breaks=seq(0,12,2))+
  xlab("\nNumbwe of Pups\n") + # X axis label
  ylab("Exosomes/ml\n") + # Y axis label
  ggtitle("Linear Regression of Exosome \nConcentration vs. Number of Pups")+ #title
  labs(color="Day")#Label table title

mean_placenta <- tc %>% 
  filter(Day %in% c('10','14','17') & !Sample_ID %in% c('70','73','74','75')) %>%
  select(-(TEI_Day:Pup_right),-Resorp) %>% 
  gather("Placenta_avg","Plac_weight", 3:5) %>%
  group_by(Day,Sample_ID) %>% 
  summarise(N = length(Plac_weight),
            mean_plac = mean(Plac_weight*1000, na.rm = TRUE), #convert g to mg
            sd = sd(Plac_weight)*1000,  #convert g to mg
            se = sd/sqrt(N))
mean_placenta %>% 
  inner_join(test7) %>% 
  ggplot(aes(x= mean_plac, y = particle_conc))+
  geom_point(size= 3,aes(color=factor(Day)))+
  geom_smooth(method = "lm", level = 0.95)+
  xlab("\nPlacental Weight (mg)\n") + # X axis label
  ylab("\nExosomes/ml\n") + # Y axis label
  ggtitle("Plasma Exosome Concentration\nThroughout Pregnancy\n")+ #title
  labs(color="G.D.")#Label table title

Joining, by = c("Day", "Sample_ID")
joining factor and character vector, coercing into character vector

mean_placenta %>% 
  inner_join(test7) %>% 
  ggplot(aes(x = Pups, y = particle_conc))+
  geom_point(size= 3,aes(color=factor(Day)))+
  geom_smooth(method = "lm", se= FALSE)+
  facet_wrap(~Day)+
  scale_x_continuous(breaks=seq(1,12,2))+
  xlab("\nNumbr of Pups\n") + # X axis label
  ylab("\nExosomes/ml\n") + # Y axis label
  ggtitle("Plasma Exosome Concentration\nThroughout Pregnancy\n")+ #title
  labs(color="G.D.")#Label table title

Joining, by = c("Day", "Sample_ID")
joining factor and character vector, coercing into character vector

test3 %>% 
  filter(!Day %in% c('1','20')) %>% 
  group_by(Day) %>% 
  ggplot(aes(factor(Day),particle_conc, color=Day)) +
  geom_point(size=3)+
  xlab("\nDay of Gestation\n") + # X axis label
  ylab("\nExosomes/ml\n") + # Y axis label
  ggtitle("Plasma Exosome Concentration\nThroughout Pregnancy\n")+ #title
  labs(color="Condition")#Label table title

nanosight_plot <- test1 %>%
  filter(Sample_ID == '75') %>% 
  ggplot(aes(x=particle_size, y=tech_mean,color=Injection ))+ #plot
  geom_ribbon(aes(ymin=tech_mean-tech_se, ymax=tech_mean+tech_se),alpha=0.2,fill = alpha('grey12', 0.2)) + #error bars
  geom_line(size=1.0) + xlim(0,500)+ #line size, x-axis scale
  scale_y_continuous(expand=c(0,0))+ #set bottom of graph
  xlab("Particle Size (nm)") + # X axis label
  ylab("\nMean Particle Concentration/ml\n") + # Y axis label
  ggtitle("Nanosight Histogram of\nVirgin Mouse Plasma")+ #title
  labs(color="Injection")+ #Label table title
  facet_wrap( ~Injection)
nanosight_plot
ggsave("Nanosight_plot.png", height = 5, width = 7, units = "in", dpi = 600)